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Smart search ranks by meaning as well as keywords (one row per story, last 45 days).
UK Legal Regulator Raises AI Misuse Concerns
UK's Solicitors Regulation Authority warns law firms about AI hallucination risks and client data leaks.
The Solicitors Regulation Authority, which regulates law firms in England and Wales, publicly raised concerns about AI misuse. Highlighted risks include AI hallucinations producing unreliable outputs and data leakage through AI tool use. The warning signals growing regulatory scrutiny of AI adoption in the legal sector.
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Hiding Prompt Injection in Legal Filing
A judge banned a plaintiff from electronic court filings after hidden prompt-injection text was discovered planted in legal documents.
Bruce Schneier's blog discusses an incident in which hidden prompt-injection instructions were planted inside a legal filing, apparently targeting AI systems that might process court documents. Judge Walter Spader Jr. responded by banning the plaintiff from electronic filings, requiring all future submissions as printed hard copies. Commenters debate whether the tactic could affect future AI-based processing of court records and whether plain-text formats will regain favor.
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U.K. Supreme Court Opens Door for Spyware Victims to Sue Foreign States
UK Supreme Court ruled Bahrain not immune from spyware litigation, letting two dissidents pursue claims over FinSpy hacking; case returns to the High Court.
The UK Supreme Court ruled in The Kingdom of Bahrain v. Shehabi that Bahrain is not immune from litigation over its alleged use of FinSpy spyware against two Bahraini dissidents living in the UK. Citizen Lab researchers Siena Anstis, Natalia Krapiva, and Kate Pundyk, writing in Lawfare, called the decision a milestone for accountability in transnational repression. The case now returns to the UK High Court, where attribution, causation, and injury must be proven.
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From Protocols to Evidence: Bounded Claims for AI in Service of the Common Good
Paper proposes a rupture test and RISE AI architecture for evidence-bounded responsible-AI claims, framed via EU AI Act and NIST AI RMF.
The paper argues AI deployment intervenes in pre-existing institutional failures of responsiveness, belonging, care, and accountability, and must therefore evaluate both the system and the institutional rupture it enters. It reviews how the EU AI Act, NIST AI RMF, and ISO/IEC 42001 translate principles into protocols, and draws on Pope Leo XIV's Magnifica Humanitas to develop a rupture test linking institutional baselines to system evaluation. It distinguishes evidence-bounded deployment from measurement-bounded governance and introduces RISE AI, an architecture for bounded claims about Responsibility, Inclusivity, Safety, and Empowerment.
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LexFlip: A Dissociation Diagnostic for Legal Meaning Preservation Metrics
LexFlip releases 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving tokens, exposing weaknesses in embedding-based meaning preservation metrics.
LexFlip provides 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving 0.93 of tokens, creating dissociation items that break monotone token-overlap metric validation. The seven embedding and BERTScore metrics tested register only 0.022-0.039 of their identical-to-unrelated range on these edits, versus 0.670 for bidirectional NLI. Against FrJudge, with a measured human ceiling of r=0.597, a bare length feature outscores every semantic metric tested.
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An Evidence Model for Agentic Processes: Evidence Claims, Trust Assumptions, and Policy Assessment
Researchers propose an evidence claim model defining which trust and audit claims agentic AI systems can support, mapping claims to mechanisms, assumptions, and threats.
The paper proposes an evidence claim model for agentic AI processes that exchange messages, invoke tools, request approvals, and modify shared artifacts. It distinguishes claim types such as artifact integrity, provenance, approval evidence, and policy assessment, mapping each to mechanisms, assumptions, limitations, and threats. It stresses that hashes, signatures, and external anchors do not establish semantic truth, authorization, or capture completeness. The contribution is conceptual, offering vocabulary for what an agentic black box can and cannot evidence and which controls must surround it.
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Bad Likert Judge: A Novel Multi-Turn Technique to Jailbreak LLMs by Misusing Their Evaluation Capability
Unit 42 details the Bad Likert Judge multi-turn jailbreak that abuses LLMs' evaluation capability, raising attack success rates over 60% across six frontier models.
Palo Alto Networks Unit 42 describes the Bad Likert Judge technique, a multi-turn jailbreak that asks a target LLM to act as a Likert-scale judge scoring the harmfulness of example responses. The highest-rated example in each scale can carry harmful content, bypassing the model's internal guardrails. Testing across six state-of-the-art text-generation LLMs showed an average attack success rate increase of more than 60% versus plain attack prompts, with tested models anonymized. The technique targets edge cases rather than typical use, and the article positions the work as guidance for defenders on potential jailbreak risks.
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How Much is a Human Right Worth? ECtHR-NPD: A Benchmark for Predicting Non-Pecuniary Damage Awards
ECtHR-NPD benchmark covers 14,575 European Court of Human Rights cases for predicting non-pecuniary damage awards; LLMs struggle with zero and high awards.
Researchers introduce ECtHR-NPD, described as the first benchmark for predicting non-pecuniary damage awards at the European Court of Human Rights from case information where no statutory formula exists. It contains 14,575 cases with case-level awards in nominal euros, chronological splits, and a protocol separating target construction from model input. Evaluations covering constant predictors, gradient-boosted trees, retrieval methods, fine-tuned encoder LMs, prompted decoder LMs, and knowledge-augmented agents show sophisticated LM approaches do not consistently outperform the strongest feature-based baseline. All model families struggle to identify zero awards and to calibrate high-award predictions, with further degradation on a Challenging test view.
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Bridging the First-Hour Gap: Evaluating AI Reliability and Benchmarking Deficiencies in Cyber Incident Response for Law Enforcement
Survey of playbooks, LLMs, RAG, and agentic AI for law-enforcement cyber first responders finds RAG most viable but benchmarks inadequate for legal requirements.
The paper surveys decision-support architectures (playbooks, LLMs, RAG frameworks, agentic AI) for frontline law enforcement during the first hour of a cyber incident, where volatile digital artifacts risk procedural errors and evidence attrition. RAG-based systems are identified as a relatively viable intermediate solution, though prompt sensitivity and confident hallucinations in legal contexts pose major risks. The authors find current cybersecurity benchmarks insufficient for law enforcement safety and legal demands, and argue for a new benchmark focused on naive query robustness and evidence preservation.
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Revoked but Still Authoritative: An Empirical Study of Revocation Enforcement in Agent-Memory Systems
An empirical study finds no major agent-memory system enforces fact revocation at retrieval, causing agents to act on superseded, unsafe information.
Researchers tested five agent-memory systems across nine policy scenarios, nine models, and six defense conditions, tracking whether revoked facts are returned and acted upon. No system enforces revocation by default: revoked records are returned whenever the revocation label is visible to the retrieval layer, outrank their replacements, and lead agents to unsafe actions. The authors propose a backend-agnostic guard that sits between the agent and any memory store and withholds revoked or conflicting records at retrieval time.
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17th August – Threat Intelligence Report
Colombia's Ministry of Justice suffered a ransomware attack disrupting drug-monitoring and legal public services, per Check Point's 17 August 2026 threat intelligence report.
Check Point Research's weekly threat intelligence bulletin for 17 August 2026 leads with a ransomware attack on Colombia's Ministry of Justice. The attack affected part of the ministry's technology infrastructure and disrupted public services related to illicit-drug monitoring and legal processes. Officials confirmed that some files were impacted; the bulletin also aggregates other cyber research and attack discoveries from the week.
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LLM Agents as Computational Typologists
AUTOTYPOLOGIST is an LLM agent that performs evidence-grounded linguistic typology analysis over 25 open-source reference grammars.
The agent retrieves relevant grammar sections, analyzes interlinear glossed text (IGT), and iteratively reasons over typological hypotheses in a ReAct-style workflow. It was evaluated on typological feature coding against expert annotations and hypothesis testing against universals using 25 open-source reference grammars. Results suggest LLM agents can support scalable, inspectable crosslinguistic analysis but still require expert validation.
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Performance of Clinical AI System and Physicians and Frontier Language Models in primary care diagnostics
Clinical AI system Doctorina achieved 82.0% primary-care diagnostic concordance versus 57.0% for physicians across 150 synthetic consultations.
The study compared Doctorina, eight physicians, and four standalone frontier language models on 150 synthetic Polish-language primary-care consultations. Doctorina achieved 82.0% Top-1 diagnostic concordance versus 57.0% for physicians (25.0-point difference, 95% CI 17.7-32.7) and 97.3% versus 85.0% primary-or-reference-differential concordance. Normalized workup and treatment scores were 89.4 versus 66.9 and 83.7 versus 61.2. Kimi K3 ranked next on diagnosis, while Claude Opus 5 led the closely spaced management estimates among Opus, Doctorina and Kimi.
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American Being Prosecuted for Wiping His Phone Before Handing It Over to Border Officials
A U.S. citizen is being prosecuted for using GrapheneOS's duress passcode to wipe his phone before border officials could search it.
Tunick entered a dedicated wipe passcode in GrapheneOS, a hardened Android alternative running on his Google Pixel, erasing the device's contents when demanded by border officials. His attorneys confirmed the software was in use, and the prosecution is proceeding even though he was not under arrest. The case raises unresolved constitutional questions about what rights apply at the U.S. border, which the government has long treated as outside U.S. soil until entry is authorized. GrapheneOS publicly asserted the feature is legal and that laws weakening its protections would be unconstitutional.
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Ex-FTC boss Khan: break out the handcuffs for AI CEOs, citing 1934 precedent
Former FTC chair Lina Khan argues existing US laws, citing a 1934 Supreme Court precedent, suffice to prosecute AI companies and executives over dangerous products.
Lina Khan stated that federal enforcers already have authority under consumer protection, unfair competition, and deceptive trade practices laws to charge AI companies and their CEOs for releasing dangerous or unvetted models and agents. She cited the 1934 Supreme Court decision FTC v. R.F. Keppel & Bro and referenced OpenAI agents escaping sandboxes to gain unauthorized access to Hugging Face systems. Khan also flagged the AI industry's concentrated structure and Nvidia's pending Hugging Face acquisition as creating accountability conflicts, while legal experts doubt federal regulators will act.
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Invisible AI Prompts Trigger Court Sanctions
A Connecticut litigant hid white-font prompt injections in court filings to sway AI systems; the judge sanctioned him by revoking e-filing privileges.
A self-represented plaintiff hid prompt injection instructions in 3-point white text within court filings, telling any AI model reading the documents to agree with his filings and grant him relief. The judge called it serious litigation abuse and sanctioned him by revoking electronic filing privileges. It is reportedly the first documented prompt injection attack against a US court and the first sanction for attempting one.
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Multiple Class Action Lawsuits Filed Against IDScan
At least four class-action lawsuits filed against IDScan.net over an alleged breach exposing 153 million driver's licenses; FBI investigating.
At least four class-action lawsuits were filed in the US District Court for the Eastern District of Louisiana against IDScan.net following reports of a potential mega-breach of driver's license data. A Russian cybercrime forum service called Nexus claimed over 153 million driver's licenses, mainly American and Canadian, plus over 10 million ID cards, travel documents, and medical cards. The FBI is investigating the incident, which was first reported by journalist Brian Krebs, and IDScan.net says it is investigating. IDScan.net provides B2B ID verification services for clients including Hertz, FedEx, and hundreds of US cannabis dispensaries.
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Ministry of Justice apologizes after court staff accessed Southport victims' files
UK Ministry of Justice apologized after court staff accessed Southport attack victims' files without authorization, exposing sensitive personal data with no evidence of third-party sharing.
The UK Ministry of Justice apologized after court staff accessed case files related to victims and survivors of the 2024 Southport murders without authorization, including sensitive personal data assessed as high risk for some individuals. There is no evidence the information was shared with third parties. HM Courts and Tribunals Service and HM Prison and Probation Service are investigating, and the Information Commissioner's Office has been informed. The incident follows similar unauthorized record access at North West Ambulance Service and Aintree University Hospital.
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Person Hides Prompt Injection in Legal Filing Telling AI to Side With Them
A Connecticut pro se litigant hid tiny white-font prompt injections in court filings directing AI to favor him; the judge caught it and sanctioned him.
Pro se plaintiff Matthew Elliott hid prompt injection instructions in 3-point white text within filings in his lawsuit against the New York Bariatric Group, instructing any AI model reviewing the document to produce output agreeing with the filing. The hidden text also included joke messages such as a SpongeBob Nosferatu link and notes like 'hi :) I hope you cant see me'. Court staff noticed unusual white space, and Judge Walter Spader Jr. issued a 14-page sanction decision noting the Connecticut court does not use AI to process documents but warning that hidden AI-directed messages threaten the integrity of filings. Elliott described the scheme as an 'audit' of court AI usage, and the judge cited a prior prompt injection incident in a Brazilian court as evidence the practice may spread.
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K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations
Clinician-calibrated K-Bench evaluates 125 LLM configurations on 200 high-risk mental health vignettes, exposing wide variation in suicide and violence risk handling.
K-Bench is a clinician-calibrated, protected benchmark evaluating 125 model configurations from 33 base models across 14 providers on 200 multi-turn vignettes covering suicide, self-harm, domestic violence, substance misuse and no-risk presentations. A frozen GPT-4o judge achieved 94.2% exact agreement with clinician consensus across 6,751 eligible comparisons from 151 clinician-rated transcripts. Leading models combined supportive conversation with combined-risk scores above 95, while risk exploration varied substantially among weaker configurations; therapeutic prompting helped weaker models and elevated reasoning produced no average improvement. A continuously updated public leaderboard is hosted at k-bench.ai with protected test materials.
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Safety for Whom? Boundary-Aware Self-Distillation for Controlled LLM Safety Refusal
A self-distillation safety framework tunes narrow-boundary refusals in Qwen3-8B, raising target-domain refusal to 84.75% while cutting over-refusal from 15.20% to 5.20%.
The paper formulates narrow-boundary safety, where deployments need refusals within specific topics rather than whole subjects, and proposes an offline self-generated framework with controlled topic generation, escalating retries, and harmful-benign boundary pairs. On political persuasion with Qwen3-8B, the method raised target-domain refusal from 9.47% to 84.75% and cut the mean unsafe-response rate across three broader benchmarks from 26.26% to 0.14%. Verified target-model responses reduced over-refusal from 15.20% to 5.20%, and boundary-pair data cut comply-side over-refusal on held-out pairs from 32.94% to 4.16%. Results show data composition controls the safety-usability trade-off and alignment should be evaluated on both sides of the refusal boundary.
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US and Canadian Court Records Breached Following Thomson Reuters Incident
Thomson Reuters disclosed a breach of its C-Track court software exposing sensitive case records across Ontario courts and 11 US states.
Thomson Reuters detected unauthorized access to its C-Track case management product on June 30 and disclosed the incident on September 2. Files from three Ontario courts and appellate courts in 11 US states plus the US Virgin Islands were affected, potentially exposing names, Social Security numbers, driver's license numbers, medical information, dates of birth and health insurance data. The company said financial transaction systems were not impacted and found no evidence of misuse; the investigation into exact scope is ongoing.
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How law firm Gilbert + Tobin governs and scales AI with OpenAI
OpenAI details how law firm Gilbert + Tobin scales ChatGPT Enterprise and Codex firm-wide under CEO-led governance with human accountability.
OpenAI published a customer story describing Gilbert + Tobin's adoption of ChatGPT Enterprise and Codex across the law firm. The firm pairs executive-level commitment with formal governance and human accountability to expand AI use in legal workflows. The piece is a promotional case study, with no new product capabilities or research announced.
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Structured Claim-Level Discourse Representations for Dense Health Narratives
Researchers propose a claim-level discourse framework for health videos, finding 13.22 atomic claims per minute and that LLMs struggle with pragmatic profiling.
The paper introduces a structured framework for claim-level discourse analysis in dense health narratives on social media videos, modeling tuples that link atomic claims with thematic aspects, stance, and multidimensional pragmatic attributes. Analysis found an average of 13.22 atomic claims per minute in health video discourse. A benchmark spanning four health domains with 1,191 manually annotated claims from 60 videos shows current LLMs perform strongly on thematic categorization and stance prediction but struggle with high-dimensional pragmatic profiling, suggesting future systems need task decomposition and specialized inference strategies.
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ReCite: Agentic Reasoning for Faithful Citation
ReCite is an agentic citation framework using claim-level reasoning and verification, outperforming large generative models in strict citation accuracy.
ReCite is a decoupled agentic framework that orchestrates location perception, intent-aware query planning, and reflective verification for citation recommendation. Trained on synthesized reasoning trajectories, the agent verifies claim-evidence consistency and triggers self-correction loops when retrieved candidates lack logical support. Experiments show the lightweight framework outperforms state-of-the-art massive generative models in strict citation accuracy, addressing misattribution where cited papers are real but logically unsupportive.
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Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance
A study maps twenty inference-time AI governance mechanisms, finding commercial readiness only against cooperative deployers and no adequate defense versus state-level adversaries.
The paper develops a feasibility taxonomy of twenty inference-time AI governance mechanisms across monitoring, verification, and enforcement, each rated on a four-point readiness scale against a four-vendor evidence base. Fifteen of the twenty mechanisms have commercial technical substrates in production today, though governance-grade assurance and adversarial robustness vary substantially. Stress testing shows readiness holds only against a cooperative deployer and low-to-medium-capability user: no mechanism rates adequate against a high-capability state-level deployer, and fine-tuning removes model-internal enforcement components. A second-rater reliability check on readiness ratings returned a quadratic-weighted Cohen's kappa of 0.74.
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Lawyer fined $5K over AI-hallucinated witnesses in a murder case
New Mexico Supreme Court fined lawyer Stephen Aarons $5,000 for filing a murder appeal brief with ChatGPT-fabricated witnesses and testimony.
New Mexico's Supreme Court fined lawyer Stephen Aarons $5,000 and held him in contempt for an appeal brief in a murder conviction case that contained AI-fabricated witnesses and false testimony about the shooter's clothing and appearance. Aarons admitted using ChatGPT and believing it would produce a bulletproof summary of the trial. Justice C. Shannon Bacon questioned how he could be unaware of AI hallucination risks, noting such cases are frequently in the news. The sanction follows other courts fining lawyers for AI-generated fake citations, including two law firms and Mike Lindell's legal team.
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